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Cognitive Modeling and
                                                                                 Robust Decision Making


                                                                                                          5 March 2012


                                                                                                             Jay Myung
                                                                                                      Program Manager
                                                                                                            AFOSR/RSL
         Integrity  Service  Excellence                                                 Air Force Research Laboratory


13 July 2012        DISTRIBUTION A: Approved for public release; distribution is unlimited.                               1
2012 AFOSR SPRING REVIEW

NAME: Jay Myung, PhD                                                               Years as AFOSR PM: 0.75


BRIEF DESCRIPTION OF PORTFOLIO:
Support experimental and formal modeling work in:
    1) Understanding cognitive processes underlying human performance in
       complex problem solving and decision making tasks;
    2) Achieving maximally effective symbiosis between humans and machine
       systems in decision making;
    3) Creating robust intelligent autonomous systems that achieve high
       performance and adapt in complex and dynamic environments.

LIST SUB-AREAS IN PORTFOLIO:

    • Mathematical Modeling of Cognition and Decision (3003B)
    • Human-System Interface and Robust Decision Making (3003H)
    • Robust Computational Intelligence (3003D)



             DISTRIBUTION A: Approved for public release; distribution is unlimited.                         2
Program Roadmap
Adaptive, Natural or Artificial, Intelligence as Computational
Algorithms Requiring Interdisciplinary Approach
General purpose algorithms that the brain uses to
achieve adaptive intelligent computation.

                    Behavior/cognition
                                                                        M
                                                                         I
                            Computation                                   N
                                                                           D          Mind as adaptive computational
                                    Neuroscience                                      algorithm (software) running
                                                                                      on the brain (hardware)

 Behavior/Cognition: extracting and defining the problem
   to be solved, consolidated as robust empirical laws.
 Computation: solves a well-defined problem, in terms
   of optimization, estimation, statistical inference, etc.
 Neuroscience: implementing the solution by the neural
   architecture (including hardware and currency).
            DISTRIBUTION A: Approved for public release; distribution is unlimited.                                    3
Computational Algorithms for
                    Cognition and Decision
Challenge for an Adaptive Intelligent System to Solve:

    Massive                           Useful                                          Actionable    Robust
   Raw Data                        Information                                        Knowledge    Decision


(1) Neuronal signal processing algorithms: optimal balance
   between representation (encoding) and computation (decoding)
   in information processing of the brain.

(2) Causal reasoning and Bayesian algorithms: optimal fusion
    of prior knowledge with data/evidence for reasoning and
    prediction under uncertainty, ambiguity, and risk.

(3) Categorization/classification algorithms: optimal generalization
    from past observations to future encounters by regulating the
    complexity of classifiers while achieving data-fitting performance.
            DISTRIBUTION A: Approved for public release; distribution is unlimited.                           4
Program Trends

• Memory, Categorization, and Reasoning

• Optimal Planning/Control and Reinforcement Learning

• Mathematical Foundation of Decision Under Uncertainty

• Causal Reasoning and Bayesian/Machine Learning Algorithms

• Neural Basis of Cognition and Decision

• Computational Cognitive Neuroscience

• Computational Principles of Intelligence and Autonomy

• Cognitive Architectures

• Software System Architectures and Sensor Networks
           DISTRIBUTION A: Approved for public release; distribution is unlimited.   5
Mathematical Modeling of Cognition and
                     Decision (3003B)

Goal:
Advance mathematical models of human performance in
attention, memory, categorization, reasoning, planning, and decision
making.
Challenges and Strategy:
• Seek algorithms for adaptive intelligence inspired by brain science
• Multidisciplinary efforts cutting across mathematics, cognitive
science, neuroscience, and computer science.




            DISTRIBUTION A: Approved for public release; distribution is unlimited.   6
Neuronal Basis of Cognition
                           (Aurel Lazar, Columbia EECS)
                                          Neuronal Signal Processing
                                      (Hodgkin & Huxley, 1963, Nobel Prize)




Prof. Aurel
  Lazar


         “Cognition is an End-product of Neural Computation.”




              DISTRIBUTION A: Approved for public release; distribution is unlimited.   7
Neuronal Basis of Cognition
                      (Aurel Lazar, Columbia EECS)

The Scientific Challenge: The Holy Grail of Neuroscience

 - What is the neural code?

 - Can we “reconstruct” cognition from neural spiking data?




                                                      ?



         DISTRIBUTION A: Approved for public release; distribution is unlimited.   8
Neuronal Basis of Cognition
                         (Aurel Lazar, Columbia EECS)
Neural Population Coding Hypothesis                                          “Brain as A/D and D/A Signal Converters”




            DISTRIBUTION A: Approved for public release; distribution is unlimited.                                     9
Neuronal Basis of Cognition
                         (Aurel Lazar, Columbia EECS)

Video Stream Recovery from Spiking Neuron Model




• First-ever demonstration of natural scene recovery from spiking neuron models
based upon an architecture that includes visual receptive fields and neural
circuits with feedback.

• Scalable encoding/decoding algorithms were demonstrated on a parallel
computing platform.


            DISTRIBUTION A: Approved for public release; distribution is unlimited.   10
Human System Interface and Robust Decision
                    Making (3003H)

Goal:
Advance research on mixed human-machine systems to aid
inference, communication, prediction, planning, scheduling, and
decision making.


Challenges and Strategy:
• Seek computational principles for symbiosis of mixed human-
machine systems with allocating and coordinating requirements.
• Statistical and machine learning methods for robust reasoning and
strategic planning.




            DISTRIBUTION A: Approved for public release; distribution is unlimited.   11
Flexible, Fast, and Rational Inductive
                 Inference (Griffiths YIP, Berkeley Psy)
                 The Problem:
                  • Understand how people are capable of fast, flexible and rational
                  inductive inference.



Prof. Thomas
  Griffiths




The Scientific Challenge:
Develop a framework for making automated systems capable of solving inductive
problems (such as learning causal relationships and identifying features of images)
with the same ease and efficiency of humans.
                 DISTRIBUTION A: Approved for public release; distribution is unlimited.   12
Flexible, Fast, and Rational Inductive
            Inference (Griffiths YIP, Berkeley Psy)
A Bayesian Statistical Approach:
Analyze human inductive inference from the perspective of Bayesian statistics.
Explore Monte Carlo algorithms and nonparametric Bayesian models as an
account of human cognition. Test models through behavioral experiments.


Features:
Features are the elementary primitives of
cognition, but are often ambiguous.


Inferring a feature representation is an
inductive inference problem.


Challenge: How do you form a set of
possible representations?



             DISTRIBUTION A: Approved for public release; distribution is unlimited.   13
Flexible, Fast, and Rational Inductive
            Inference (Griffiths YIP, Berkeley Psy)
Nonparametric Bayesian Algorithms:                                                     Multi-level Category Learning
Basic idea: Use flexible hypothesis spaces
from nonparametric Bayesian models with
potentially infinite many features.
Learning by examples: Combines structure
of a bias towards simpler feature
representations, but with the flexibility to grow
in complexity as more data is observed.




             DISTRIBUTION A: Approved for public release; distribution is unlimited.                                   14
Robust Computational Intelligence (3003D)

Goal:
Advance research on machine intelligence architectures that derive
from cognitive and biological models of human intelligence.

Challenges and Strategy:
• Seek fundamental principles and methodologies for building
autonomous systems that learn and function at the level of flexibility
comparable to that of humans and animals.




            DISTRIBUTION A: Approved for public release; distribution is unlimited.   15
Practical Reasoning in
              Robotic Agents (Pagnucco, UNSW CS)

Objective: Extract summarized, actionable
knowledge from raw data in complex problem
domains (e.g., spatial mapping).

DoD benefits: Enables autonomous systems
to automatically adapt to unfamiliar situations
in the presence of erroneous information.

Technical approach: Formal A.I. methods
for integrating logical and non-logical
reasoning in robotic agents (e.g., MAVs).

                                                     Spatial Mapping




              DISTRIBUTION A: Approved for public release; distribution is unlimited.   16
Modeling Functional Architectures of
           Human Decision Making (Patterson, AFRL/RH)

Objective: Understand functional                                                                 Intuitive Decision Making:
architecture of human decision making.                                                       Synthetic Terrain Imagery & Object Sequences


DoD benefits: Increased knowledge about
designs of human-machine systems.

Technical approach: Combine analytical
and intuitive decision making within formal
tests of functional architecture.

                 Hummer

                              Truck
                                                            Rocket
      Truck         S1                            S2        Launcher
IN                                                                              OUT
      S0                              Tank         Truck                S5

      Patriot                                                 Rocket
      Launcher     S3                             S4          Launcher
                             Patriot
                             Launcher

                 Tank

                   DISTRIBUTION A: Approved for public release; distribution is unlimited.                                                  17
Joint Initiative with AFRL/RH in
                     Neuroergonomics Research
                              (Parasuraman, George Mason U Psy)

CENTEC: Center of Excellence in Neuro-Ergonomics,
        Technology and Cognition (since July 2010)




                   Goal: Support the Air Force mission of enhanced human
 Prof. Raja       effectiveness through
Parasuraman            • Research in neuroergonomics, technology, and cognition
                       • Training of graduate students and postdoctoral fellows
                       • Collaborations with scientists of AFRL/RH
                  • “Direct transitions” of university research to AFRL

NEUROADAPTIVE LEARNING, NEUROIMAGE, BEHAVIORAL GENETICS, ATTENTION,
TRUST IN AUTOMATION, MULTI-TASKING, MEMORY, SPATIAL COGNITION
              DISTRIBUTION A: Approved for public release; distribution is unlimited.   18
Other Organizations that Fund
                     Related Work

• ONR (PM: Paul Bello)
    • Representing and Reasoning about Uncertainty Program
    • Theoretical Foundations for Socio-Cognitive Architectures
    Program
• ONR (PM: Tom McKenna)
    • Human Robot Interaction Program
• ONR (PM: Marc Steinberg)
    • Science of Autonomy Program
 ARO (PM: Janet Spoonamore)
    • Decision and Neurosciences Program
 NSF (PMs: Betty Tuller & Lawrence Gottlob)
    • Perception, Action & Cognition Program
• NSF (PM: Sven Koenig)
    • Robust Intelligence Program



         DISTRIBUTION A: Approved for public release; distribution is unlimited.   19
Recent Highlights

CENTEC team (PI: R. Parasuraman, GMU)

   - Special issue in Neuroimage on
   neuroergonomics
   - Joint papers by GMU and AFRL/RH
   scientists




Prof. T. Walsh team (NICTA, Australia)

   - Eureka Prize for Peter Stuckey
    (Highest prize in CS in Australia)
   - Best Paper Prize @ AAAI 2011



          DISTRIBUTION A: Approved for public release; distribution is unlimited.   20
Program Summary
        Transformational Impacts and Opportunities

The Basic Premise of Investment Philosophy
  Cognition/Intelligence as computation algorithm:
  Requires multidisciplinary research efforts to uncover brain
  algorithms, from computer science, mathematics, statistics,
  engineering, and psychology.


1. Neurocomputational foundation of cognition
   •   We stand “almost” at the threshold of a major scientific
       breakthrough reminiscent of genetics in the 1950s, i.e.,
       Watson & Click (1953; Nobel Prize, 1962).
2. Self-learning decision support systems
   •   Machine learning algorithms for inference and decision
       making capability comparable to that of humans.
3. Trust-worthy autonomous agents
   •   Capable of sense-making massive raw data for
       prediction, planning, communication, and decision.

             DISTRIBUTION A: Approved for public release; distribution is unlimited.   21
Questions?



     Thank you for your attention


 Jay Myung, Program Manager, AFOSR/RSL
                          Jay.myung@afosr.af.mil




DISTRIBUTION A: Approved for public release; distribution is unlimited.   22
Back-up Slides to Follow




DISTRIBUTION A: Approved for public release; distribution is unlimited.   23
Neuronal Basis of Cognition
                            (Aurel Lazar, Columbia EECS)

Six Disruptive Basic Research Areas
(Honorable Lemnios, Asst Sec Def. (R&E))

   1. Metamaterials and                                                      3. Cognitive Neuroscience:
      Plasmonics
                                                                             More deeply understand and more fully
   2. Quantum Information                                                    exploit the fundamental mechanisms of
      Science
                                                                             the brain.
   3. Cognitive Neuroscience
   4. Nanoscience and                                                        • Key Research Challenges:
      Nanoengineering                                                         - Solving the inverse problem of
   5. Synthetic Biology                                                         predicting human behavior from
   6. Computational Modeling                                                    brain signals
      of Human and Social                                                     - Translating clinical measurements &
      Behavior
                                                                                analyses to uninjured personnel
                                                                              - Developing models incorporating
                                                                                individual brain variability




               DISTRIBUTION A: Approved for public release; distribution is unlimited.                                24
IMPLICIT LEARNING of ARTIFICIAL GRAMMAR
                                                   (e.g., Reber, 1967)

           Rules for „sentence‟ construction: Finite State Algorithm

                                                   P
                                                              T
                                   T              S1                                S2       S
                IN                                                                                        OUT
                         S0                                                                          S5
                                                                             X           P

                               V                S3                                  S4           S
                                                                   V
                             X
Sentences of length 6-8 used (34 total possible); examples: TPTXVPS; VXXVPS
Participants learned the grammar with fewer errors than participants who learned
random letter strings—but few could provide answers about the rules
Pattern 'chunks' can be abstracted via a rudimentary inductive process
without explicit strategies

                                         Distribution A
          DISTRIBUTION A: Approved for public release; distribution is unlimited.                               25
CHARACTERISTICS OF 2 PROCESSES
                                       (derived from Evans, 2008)

“System 1” (Intuitive)                                         System 2” (Analytic)
*Situational Pattern Recog                                     *Deliberative
Unconscious                                                    Conscious
Implicit                                                       Explicit
Automatic                                                      Controlled
Low effort                                                     High effort
Rapid                                                          Slow
High capacity                                                  Low capacity
Holistic, perceptual                                           Analytic, reflective
Domain specific (inflexible)                                   Domain specific & general (flexible)
Contextualized                                                 Abstract
Nonverbal                                                      Linked to language
Independent of working                                         Limited by working memory capacity
     memory


                                          Distribution A
           DISTRIBUTION A: Approved for public release; distribution is unlimited.                    26

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Myung - Cognitive Modeling and Robust Decision Making - Spring Review 2012

  • 1. Cognitive Modeling and Robust Decision Making 5 March 2012 Jay Myung Program Manager AFOSR/RSL Integrity  Service  Excellence Air Force Research Laboratory 13 July 2012 DISTRIBUTION A: Approved for public release; distribution is unlimited. 1
  • 2. 2012 AFOSR SPRING REVIEW NAME: Jay Myung, PhD Years as AFOSR PM: 0.75 BRIEF DESCRIPTION OF PORTFOLIO: Support experimental and formal modeling work in: 1) Understanding cognitive processes underlying human performance in complex problem solving and decision making tasks; 2) Achieving maximally effective symbiosis between humans and machine systems in decision making; 3) Creating robust intelligent autonomous systems that achieve high performance and adapt in complex and dynamic environments. LIST SUB-AREAS IN PORTFOLIO: • Mathematical Modeling of Cognition and Decision (3003B) • Human-System Interface and Robust Decision Making (3003H) • Robust Computational Intelligence (3003D) DISTRIBUTION A: Approved for public release; distribution is unlimited. 2
  • 3. Program Roadmap Adaptive, Natural or Artificial, Intelligence as Computational Algorithms Requiring Interdisciplinary Approach General purpose algorithms that the brain uses to achieve adaptive intelligent computation. Behavior/cognition M I Computation N D Mind as adaptive computational Neuroscience algorithm (software) running on the brain (hardware) Behavior/Cognition: extracting and defining the problem to be solved, consolidated as robust empirical laws. Computation: solves a well-defined problem, in terms of optimization, estimation, statistical inference, etc. Neuroscience: implementing the solution by the neural architecture (including hardware and currency). DISTRIBUTION A: Approved for public release; distribution is unlimited. 3
  • 4. Computational Algorithms for Cognition and Decision Challenge for an Adaptive Intelligent System to Solve: Massive Useful Actionable Robust Raw Data Information Knowledge Decision (1) Neuronal signal processing algorithms: optimal balance between representation (encoding) and computation (decoding) in information processing of the brain. (2) Causal reasoning and Bayesian algorithms: optimal fusion of prior knowledge with data/evidence for reasoning and prediction under uncertainty, ambiguity, and risk. (3) Categorization/classification algorithms: optimal generalization from past observations to future encounters by regulating the complexity of classifiers while achieving data-fitting performance. DISTRIBUTION A: Approved for public release; distribution is unlimited. 4
  • 5. Program Trends • Memory, Categorization, and Reasoning • Optimal Planning/Control and Reinforcement Learning • Mathematical Foundation of Decision Under Uncertainty • Causal Reasoning and Bayesian/Machine Learning Algorithms • Neural Basis of Cognition and Decision • Computational Cognitive Neuroscience • Computational Principles of Intelligence and Autonomy • Cognitive Architectures • Software System Architectures and Sensor Networks DISTRIBUTION A: Approved for public release; distribution is unlimited. 5
  • 6. Mathematical Modeling of Cognition and Decision (3003B) Goal: Advance mathematical models of human performance in attention, memory, categorization, reasoning, planning, and decision making. Challenges and Strategy: • Seek algorithms for adaptive intelligence inspired by brain science • Multidisciplinary efforts cutting across mathematics, cognitive science, neuroscience, and computer science. DISTRIBUTION A: Approved for public release; distribution is unlimited. 6
  • 7. Neuronal Basis of Cognition (Aurel Lazar, Columbia EECS) Neuronal Signal Processing (Hodgkin & Huxley, 1963, Nobel Prize) Prof. Aurel Lazar “Cognition is an End-product of Neural Computation.” DISTRIBUTION A: Approved for public release; distribution is unlimited. 7
  • 8. Neuronal Basis of Cognition (Aurel Lazar, Columbia EECS) The Scientific Challenge: The Holy Grail of Neuroscience - What is the neural code? - Can we “reconstruct” cognition from neural spiking data? ? DISTRIBUTION A: Approved for public release; distribution is unlimited. 8
  • 9. Neuronal Basis of Cognition (Aurel Lazar, Columbia EECS) Neural Population Coding Hypothesis “Brain as A/D and D/A Signal Converters” DISTRIBUTION A: Approved for public release; distribution is unlimited. 9
  • 10. Neuronal Basis of Cognition (Aurel Lazar, Columbia EECS) Video Stream Recovery from Spiking Neuron Model • First-ever demonstration of natural scene recovery from spiking neuron models based upon an architecture that includes visual receptive fields and neural circuits with feedback. • Scalable encoding/decoding algorithms were demonstrated on a parallel computing platform. DISTRIBUTION A: Approved for public release; distribution is unlimited. 10
  • 11. Human System Interface and Robust Decision Making (3003H) Goal: Advance research on mixed human-machine systems to aid inference, communication, prediction, planning, scheduling, and decision making. Challenges and Strategy: • Seek computational principles for symbiosis of mixed human- machine systems with allocating and coordinating requirements. • Statistical and machine learning methods for robust reasoning and strategic planning. DISTRIBUTION A: Approved for public release; distribution is unlimited. 11
  • 12. Flexible, Fast, and Rational Inductive Inference (Griffiths YIP, Berkeley Psy) The Problem: • Understand how people are capable of fast, flexible and rational inductive inference. Prof. Thomas Griffiths The Scientific Challenge: Develop a framework for making automated systems capable of solving inductive problems (such as learning causal relationships and identifying features of images) with the same ease and efficiency of humans. DISTRIBUTION A: Approved for public release; distribution is unlimited. 12
  • 13. Flexible, Fast, and Rational Inductive Inference (Griffiths YIP, Berkeley Psy) A Bayesian Statistical Approach: Analyze human inductive inference from the perspective of Bayesian statistics. Explore Monte Carlo algorithms and nonparametric Bayesian models as an account of human cognition. Test models through behavioral experiments. Features: Features are the elementary primitives of cognition, but are often ambiguous. Inferring a feature representation is an inductive inference problem. Challenge: How do you form a set of possible representations? DISTRIBUTION A: Approved for public release; distribution is unlimited. 13
  • 14. Flexible, Fast, and Rational Inductive Inference (Griffiths YIP, Berkeley Psy) Nonparametric Bayesian Algorithms: Multi-level Category Learning Basic idea: Use flexible hypothesis spaces from nonparametric Bayesian models with potentially infinite many features. Learning by examples: Combines structure of a bias towards simpler feature representations, but with the flexibility to grow in complexity as more data is observed. DISTRIBUTION A: Approved for public release; distribution is unlimited. 14
  • 15. Robust Computational Intelligence (3003D) Goal: Advance research on machine intelligence architectures that derive from cognitive and biological models of human intelligence. Challenges and Strategy: • Seek fundamental principles and methodologies for building autonomous systems that learn and function at the level of flexibility comparable to that of humans and animals. DISTRIBUTION A: Approved for public release; distribution is unlimited. 15
  • 16. Practical Reasoning in Robotic Agents (Pagnucco, UNSW CS) Objective: Extract summarized, actionable knowledge from raw data in complex problem domains (e.g., spatial mapping). DoD benefits: Enables autonomous systems to automatically adapt to unfamiliar situations in the presence of erroneous information. Technical approach: Formal A.I. methods for integrating logical and non-logical reasoning in robotic agents (e.g., MAVs). Spatial Mapping DISTRIBUTION A: Approved for public release; distribution is unlimited. 16
  • 17. Modeling Functional Architectures of Human Decision Making (Patterson, AFRL/RH) Objective: Understand functional Intuitive Decision Making: architecture of human decision making. Synthetic Terrain Imagery & Object Sequences DoD benefits: Increased knowledge about designs of human-machine systems. Technical approach: Combine analytical and intuitive decision making within formal tests of functional architecture. Hummer Truck Rocket Truck S1 S2 Launcher IN OUT S0 Tank Truck S5 Patriot Rocket Launcher S3 S4 Launcher Patriot Launcher Tank DISTRIBUTION A: Approved for public release; distribution is unlimited. 17
  • 18. Joint Initiative with AFRL/RH in Neuroergonomics Research (Parasuraman, George Mason U Psy) CENTEC: Center of Excellence in Neuro-Ergonomics, Technology and Cognition (since July 2010)  Goal: Support the Air Force mission of enhanced human Prof. Raja effectiveness through Parasuraman • Research in neuroergonomics, technology, and cognition • Training of graduate students and postdoctoral fellows • Collaborations with scientists of AFRL/RH • “Direct transitions” of university research to AFRL NEUROADAPTIVE LEARNING, NEUROIMAGE, BEHAVIORAL GENETICS, ATTENTION, TRUST IN AUTOMATION, MULTI-TASKING, MEMORY, SPATIAL COGNITION DISTRIBUTION A: Approved for public release; distribution is unlimited. 18
  • 19. Other Organizations that Fund Related Work • ONR (PM: Paul Bello) • Representing and Reasoning about Uncertainty Program • Theoretical Foundations for Socio-Cognitive Architectures Program • ONR (PM: Tom McKenna) • Human Robot Interaction Program • ONR (PM: Marc Steinberg) • Science of Autonomy Program  ARO (PM: Janet Spoonamore) • Decision and Neurosciences Program  NSF (PMs: Betty Tuller & Lawrence Gottlob) • Perception, Action & Cognition Program • NSF (PM: Sven Koenig) • Robust Intelligence Program DISTRIBUTION A: Approved for public release; distribution is unlimited. 19
  • 20. Recent Highlights CENTEC team (PI: R. Parasuraman, GMU) - Special issue in Neuroimage on neuroergonomics - Joint papers by GMU and AFRL/RH scientists Prof. T. Walsh team (NICTA, Australia) - Eureka Prize for Peter Stuckey (Highest prize in CS in Australia) - Best Paper Prize @ AAAI 2011 DISTRIBUTION A: Approved for public release; distribution is unlimited. 20
  • 21. Program Summary Transformational Impacts and Opportunities The Basic Premise of Investment Philosophy Cognition/Intelligence as computation algorithm: Requires multidisciplinary research efforts to uncover brain algorithms, from computer science, mathematics, statistics, engineering, and psychology. 1. Neurocomputational foundation of cognition • We stand “almost” at the threshold of a major scientific breakthrough reminiscent of genetics in the 1950s, i.e., Watson & Click (1953; Nobel Prize, 1962). 2. Self-learning decision support systems • Machine learning algorithms for inference and decision making capability comparable to that of humans. 3. Trust-worthy autonomous agents • Capable of sense-making massive raw data for prediction, planning, communication, and decision. DISTRIBUTION A: Approved for public release; distribution is unlimited. 21
  • 22. Questions? Thank you for your attention Jay Myung, Program Manager, AFOSR/RSL Jay.myung@afosr.af.mil DISTRIBUTION A: Approved for public release; distribution is unlimited. 22
  • 23. Back-up Slides to Follow DISTRIBUTION A: Approved for public release; distribution is unlimited. 23
  • 24. Neuronal Basis of Cognition (Aurel Lazar, Columbia EECS) Six Disruptive Basic Research Areas (Honorable Lemnios, Asst Sec Def. (R&E)) 1. Metamaterials and 3. Cognitive Neuroscience: Plasmonics More deeply understand and more fully 2. Quantum Information exploit the fundamental mechanisms of Science the brain. 3. Cognitive Neuroscience 4. Nanoscience and • Key Research Challenges: Nanoengineering - Solving the inverse problem of 5. Synthetic Biology predicting human behavior from 6. Computational Modeling brain signals of Human and Social - Translating clinical measurements & Behavior analyses to uninjured personnel - Developing models incorporating individual brain variability DISTRIBUTION A: Approved for public release; distribution is unlimited. 24
  • 25. IMPLICIT LEARNING of ARTIFICIAL GRAMMAR (e.g., Reber, 1967) Rules for „sentence‟ construction: Finite State Algorithm P T T S1 S2 S IN OUT S0 S5 X P V S3 S4 S V X Sentences of length 6-8 used (34 total possible); examples: TPTXVPS; VXXVPS Participants learned the grammar with fewer errors than participants who learned random letter strings—but few could provide answers about the rules Pattern 'chunks' can be abstracted via a rudimentary inductive process without explicit strategies Distribution A DISTRIBUTION A: Approved for public release; distribution is unlimited. 25
  • 26. CHARACTERISTICS OF 2 PROCESSES (derived from Evans, 2008) “System 1” (Intuitive) System 2” (Analytic) *Situational Pattern Recog *Deliberative Unconscious Conscious Implicit Explicit Automatic Controlled Low effort High effort Rapid Slow High capacity Low capacity Holistic, perceptual Analytic, reflective Domain specific (inflexible) Domain specific & general (flexible) Contextualized Abstract Nonverbal Linked to language Independent of working Limited by working memory capacity memory Distribution A DISTRIBUTION A: Approved for public release; distribution is unlimited. 26